Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network

Asher, Zachary D.; Galang, A.; Briggs, Will; Johnston, Brian; Bradley, Thomas H.; Jathar, Shantanu H.

doi:10.4271/2018-01-0315

Cited by 18 publications

(11 citation statements)

References 30 publications

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“…The gradients of the stochastic objective function at timestep t (g t ) are obtained as equation (5), then exponential moving averages of the gradient (m t ) and the squared gradient (v t ) are updated by equations (6) and (7). To solve the bias initialization problem that the moment biases toward 0 for the initial timestep, bias-correction is adopted as equation (8). Finally, the parameters at the current timestep (u t ) are updated as shown in equation 9g t = r u f t u tÀ1 ð Þ ð5Þ…”

Section: Model Structurementioning

confidence: 99%

“…Hybrid vehicle fuel economy and emissions in transient cycles have been predicted with artificial neural networks (ANNs). 8 Researchers have attempted to predict vertical and horizontal tire contact forces between vehicle tires and roads. 9 Lock-up clutch control of transmission was also a target subject using deep learning, and a deep convolutional neural network was utilized to classify time-series transmission measurement data.…”

Section: Introductionmentioning

confidence: 99%

“…Most previous studies, [6][7][8][9]13,15,16,19,21,[24][25][26] studied machine learning, and deep learning specifically, with MATLAB. Although MATLAB is easy to use, it has limitations of expandability and standardization of deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The two models have different compositions of these hyperparameters. The Knock model had 7 hidden layers, and the number of hidden nodes were8,11,14,11,8,5, and 3 at each hidden layer…”

mentioning

confidence: 99%

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Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine

Shin

Lee

Kim

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Recently, deep learning has played an important role in the rise of artificial intelligence, and its accuracy has gained recognition in various research fields. Although engine phenomena are very complicated, they can be predicted with high accuracy using deep learning because they are based on the fundamentals of physics and chemistry. In this research, models were built with deep neural networks for gasoline engine prediction. The model consists of two sub-models. The first predicts the knock occurrence, and the second predicts performance, combustion, and emissions. This includes maximum cylinder pressure, crank angle at maximum cylinder pressure, maximum pressure rise rate, and brake mean effective pressure, brake-specific fuel consumption, brake-specific nitrogen oxides, and brake-specific carbon oxide, which are representative results of the engine (for normal combustion cases without knock). Model input parameters were selected considering engine operating conditions, and physically measurable sensor values. For test cases, the accuracy of the first model for knock classification is 99.0%, and the coefficient of determination (R2) values for the second model are all above 0.99. Test times of both models were approximately 2 ms. The robustness of all the models was verified using K-fold cross-validation. A sensitivity study of accuracy, according to the amount of training utilized, was also conducted to determine how many data points are required to effectively train the deep learning model. Accordingly, a deep learning approach was applied to predict the steady-state conditions of a gasoline engine. Achieved model accuracies and robustness proved deep learning to be an effective modeling approach, and test time was recognized to be able to apply for the real-time prediction. The sensitivity analysis can be applied for the preliminary study to define the number of experimental points for the deep learning model.

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Section: Model Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The two models have different compositions of these hyperparameters. The Knock model had 7 hidden layers, and the number of hidden nodes were8,11,14,11,8,5, and 3 at each hidden layer…”

mentioning

confidence: 99%

See 2 more Smart Citations

Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine

Shin

Lee

Kim

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…Furthermore, some regression models have been employed by previous researchers to simplify the calculation mainly based on comparatively sophisticated map look-up process. For the Velocity-Acceleration Matrix Model, the engine-out emissions can be calculated as shown [34,35]: [36]. However, limited by the actual computational capacities, this emission model generally has to get accurate results at the cost of soaring time expenditure, which makes it not a practicable solution.…”

Section: Quadratic Polynomial Fitting Of Emission Datamentioning

confidence: 99%

A Novel Control Algorithm Design for Hybrid Electric Vehicles Considering Energy Consumption and Emission Performance

2019

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Under the severe challenge of increasingly stringent emission regulations and constantly improving fuel economy requirements, hybrid electric vehicles (HEVs) have attracted widespread attention in the auto industry as a practicable technical route of green vehicles. To address the considerations on energy consumption and emission performance simultaneously, a novel control algorithm design is proposed for the energy management system (EMS) of HEVs. First, energy consumption of the investigated P3 HEV powertrain is determined based on bench test data. Second, crucial performance indicators of NOx and particle emissions, prior to a catalytic converter, are also measured and processed as a prerequisite. A comprehensive objective function is established on the grounds of the Equivalent Consumption Minimization Strategy (ECMS) and corresponding simulation models are constructed in MATLAB/SIMULINK. Subsequently, the control algorithm is validated against the simulation results predicated on the Worldwide-Harmonized Light-Vehicle Test Procedure (WLTP).Integrated research contents include: (1) The searching process aimed at the optimal solution of the pre-established multi-parameter objective function is thoroughly investigated; (2) the impacts of weighting coefficients pertaining to two exhaust pollutants upon the specific configurations of the proposed control algorithm are discussed in detail; and (3) the comparison analysis of the simulation results obtained from ECMS and classical Dynamic Programming (DP), respectively, is performed.

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Component sizing of a series hybrid electric vehicle through artificial neural network

Khamesipour

Chitsaz

Salehi

et al. 2022

Energy Conversion and Management

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Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network

Cited by 18 publications

References 30 publications

Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine

Deep learning procedure for knock, performance and emission prediction at steady-state condition of a gasoline engine

A Novel Control Algorithm Design for Hybrid Electric Vehicles Considering Energy Consumption and Emission Performance

Component sizing of a series hybrid electric vehicle through artificial neural network

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